A training course for psychologists:Learning to assess (alleged) sexual abuse among victims and perpetrators who have intellectual disabilities

People with intellectual disabilities (ID) are at greater risk of being a victim of sexual abuse and may also be more predisposed to perpetrating sexual abuse. Although the prevalence of sexual abuse among people with ID is difficult to determine, it is clear that there are serious consequences for both victims and perpetrators, and professional support is needed. Psychologists play an important role in the assessment of sexual abuse in both victims and perpetrators and require specific knowledge and skills to execute the assessments. We therefore developed a training course for psychologists aimed at increasing their (applied) knowledge of sexual abuse and the related assessment process in people with ID. In a five-day training course, sessions focusing on theories about diagnostic models were combined with sessions focusing on the assessment of sexual abuse of victims and perpetrators. The effectiveness of the training course was determined in terms of (applied) knowledge via the administration of a study-specific questionnaire including a hypothetical case vignette before, immediately after, and six months after completion of the course. The results show that the knowledge of the psychologists related to sexual abuse and the assessment process for sexual abuse increased significantly, and remained above pre-test level at six-month follow-up. These results are promising, but more research is needed to see if the increased (applied) knowledge in turn leads to application in practice and better care for both victims and perpetrators

Deep Gaussian processes for biogeophysical parameter retrieval and model inversion

Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative solutions to such RTM inversion problems. This is in large part due to the confidence intervals they provide, and their predictive accuracy. However, RTMs are very complex, highly nonlinear, and typically hierarchical models, so that very often a single (shallow) GP model cannot capture complex feature relations for inversion. This motivates the use of deeper hierarchical architectures, while still preserving the desirable properties of GPs. This paper introduces the use of deep Gaussian Processes (DGPs) for bio-geo-physical model inversion. Unlike shallow GP models, DGPs account for complicated (modular, hierarchical) processes, provide an efficient solution that scales well to big datasets, and improve prediction accuracy over their single layer counterpart. In the experimental section, we provide empirical evidence of performance for the estimation of surface temperature and dew point temperature from infrared sounding data, as well as for the prediction of chlorophyll content, inorganic suspended matter, and coloured dissolved matter from multispectral data acquired by the Sentinel-3 OLCI sensor. The presented methodology allows for more expressive forms of GPs in big remote sensing model inversion problems.European Research Council (ERC) 647423Spanish Ministry of Economy and Competitiveness TIN2015-64210-R DPI2016-77869-C2-2-RSpanish Excellence Network TEC2016-81900-REDTLa Caixa Banking Foundation (Barcelona, Spain) 100010434 LCF-BQ-ES17-1160001

Integrating Physics Modelling with Machine Learning for Remote Sensing

L’observació de la Terra a partir de les dades proporcionades per sensors abord de satèl·lits, així com les proporcionades per models de transferència radiativa o climàtics, juntament amb les mesures in situ proporcionen una manera sense precedents de monitorar el nostre planeta amb millors resolucions espacials i temporals. La riquesa, quantitat i diversitat de les dades adquirides i posades a disposició també augmenta molt ràpidament. Aquestes dades ens permeten predir el rendiment dels cultius, fer un seguiment del canvi d’ús del sòl com ara la desforestació, supervisar i respondre als desastres naturals, i predir i mitigar el canvi climàtic. Per tal de fer front a tots aquests reptes, les dues darreres dècades han evidenciat un gran augment en l'aplicació d'algorismes d'aprenentatge automàtic en l'observació de la Terra. Amb l'anomenat `machine learning' es pot fer un ús eficient del flux de dades creixent en quantitat i diversitat. Els algorismes d'aprenentatge màquina, però, solen ser models agnòstics i massa flexibles i, per tant, acaben per no respectar les lleis fonamentals de la física. D’altra banda, en els darrers anys s’ha produït un augment de la investigació que intenta integrar el coneixement de física en algorismes d’aprenentatge, amb la finalitat d’obtenir solucions interpretables i que tinguin sentit físic. L’objectiu principal d’aquesta tesi és dissenyar diferents maneres de codificar el coneixement físic per proporcionar mètodes d’aprenentatge automàtic adaptats a problemes específics en teledetecció. Introduïm nous mètodes que poden fusionar de manera òptima fonts de dades heterogènies, explotar les regularitats de dades, incorporar equacions diferencials, obtenir models precisos que emulen, i per tant són coherents amb models físics, i models que aprenen parametrizacions del sistema combinant models i simulacions.Earth observation through satellite sensors, models and in situ measurements provides a way to monitor our planet with unprecedented spatial and temporal resolution. The amount and diversity of the data which is recorded and made available is ever-increasing. This data allows us to perform crop yield prediction, track land-use change such as deforestation, monitor and respond to natural disasters and predict and mitigate climate change. The last two decades have seen a large increase in the application of machine learning algorithms in Earth observation in order to make efficient use of the growing data-stream. Machine learning algorithms, however, are typically model agnostic and too flexible and so end up not respecting fundamental laws of physics. On the other hand there has, in recent years, been an increase in research attempting to embed physics knowledge in machine learning algorithms in order to obtain interpretable and physically meaningful solutions. The main objective of this thesis is to explore different ways of encoding physical knowledge to provide machine learning methods tailored for specific problems in remote sensing. Ways of expressing expert knowledge about the relevant physical systems in remote sensing abound, ranging from simple relations between reflectance indices and biophysical parameters to complex models that compute the radiative transfer of electromagnetic radiation through our atmosphere, and differential equations that explain the dynamics of key parameters. This thesis focuses on inversion problems, emulation of radiative transfer models, and incorporation of the abovementioned domain knowledge in machine learning algorithms for remote sensing applications. We explore new methods that can optimally model simulated and in-situ data jointly, incorporate differential equations in machine learning algorithms, handle more complex inversion problems and large-scale data, obtain accurate and computationally efficient emulators that are consistent with physical models, and that efficiently perform approximate Bayesian inversion over radiative transfer models

Women with a Preterm Cesarean Have High Rates of Successful Trial of Labor in a Subsequent Term Pregnancy

Objective The rate of cesareans has increased worldwide. Therefore, an increasing number of women has to decide how to deliver in a subsequent pregnancy. Individualized information on risks and success chances is helpful. This study investigates the effect of a preterm cesarean on success of subsequent term trial of labor. Study Design Ten-year Dutch cohort (2000-2009) of women with one previous cesarean and a subsequent term trial of labor. Subgroups were made based on gestational age at first cesarean delivery (25-28, 28-30, 30-32 and 32-34 weeks) and stratified based the way in which second delivery started. Rates of vaginal deliveries, maternal, and neonatal outcomes were compared with women who had a first-term cesarean (37-43 weeks). Results Four thousand three-hundred forty-two women delivered by preterm cesarean in the first pregnancy. These women had high rates of successful trial of labor, both after spontaneous onset (86.2-96.2%) and induction (72.8-75.4%). Rates of adverse outcomes were low and similar compared with women with a previous term cesarean. Conclusion In this 10-year nationwide cohort, women with a preterm first cesarean who opted for trial of labor in a subsequent pregnancy had high rates of successful trial of labor

The efficacy of therapeutic plasma exchange in COVID-19 patients on endothelial tightness in vitro is hindered by platelet activation

Coronavirus disease (COVID)-19 is characterised in particular by vascular inflammation with platelet activation and endothelial dysfunction. During the pandemic, therapeutic plasma exchange (TPE) was used to reduce the cytokine storm in the circulation and delay or prevent ICU admissions. This procedure consists in replacing the inflammatory plasma by fresh frozen plasma from healthy donors and is often used to remove pathogenic molecules from plasma (autoantibodies, immune complexes, toxins, etc.). This study uses an in vitro model of platelet-endothelial cell interactions to assess changes in these interactions by plasma from COVID-19 patients and to determine the extent to which TPE reduces such changes. We noted that exposure of an endothelial monolayer to plasmas from COVID-19 patients post-TPE induced less endothelial permeability compared to COVID-19 control plasmas. Yet, when endothelial cells were co-cultured with healthy platelets and exposed to the plasma, the beneficial effect of TPE on endothelial permeability was somewhat reduced. This was linked to platelet and endothelial phenotypical activation but not with inflammatory molecule secretion. Our work shows that, in parallel to the beneficial removal of inflammatory factors from the circulation, TPE triggers cellular activation which may partly explain the reduction in efficacy in terms of endothelial dysfunction. These findings provide new insights for improving the efficacy of TPE using supporting treatments targeting platelet activation, for instance

Histopathological evaluation of thrombus in patients presenting with stent thrombosis. A multicenter European study: a report of the prevention of late stent thrombosis by an interdisciplinary global European effort consortium

Background Stent thrombosis (ST) is a rare but serious complication following percutaneous coronary intervention. Analysis of thrombus composition from patients undergoing catheter thrombectomy may provide important insights into the pathological processes leading to thrombus formation. We performed a large-scale multicentre study to evaluate thrombus specimens in patients with ST across Europe. Methods Patients presenting with ST and undergoing thrombus aspiration were eligible for inclusion. Thrombus collection was performed according to a standardized protocol and specimens were analysed histologically at a core laboratory. Serial tissue cross sections were stained with haematoxylin–eosin (H&E), Carstairs and Luna. Immunohistochemistry was performed to identify leukocyte subsets, prothrombotic neutrophil extracellular traps (NETs), erythrocytes, platelets, and fibrinogen. Results Overall 253 thrombus specimens were analysed; 79 (31.2%) from patients presenting with early ST, 174 (68.8%) from late ST; 79 (31.2%) were from bare metal stents, 166 (65.6%) from drug-eluting stents, 8 (3.2%) were from stents of unknown type. Thrombus specimens displayed heterogeneous morphology with platelet-rich thrombus and fibrin/fibrinogen fragments most abundant; mean platelet coverage was 57% of thrombus area. Leukocyte infiltrations were hallmarks of both early and late ST (early: 2260 ± 1550 per mm2 vs. late: 2485 ± 1778 per mm2; P = 0.44); neutrophils represented the most prominent subset (early: 1364 ± 923 per mm2 vs. late: 1428 ± 1023 per mm2; P = 0.81). Leukocyte counts were significantly higher compared with a control group of patients with thrombus aspiration in spontaneous myocardial infarction. Neutrophil extracellular traps were observed in 23% of samples. Eosinophils were present in all stent types, with higher numbers in patients with late ST in sirolimus-and everolimus-eluting stents. Conclusion In a large-scale study of histological thrombus analysis from patients presenting with ST, thrombus specimens displayed heterogeneous morphology. Recruitment of leukocytes, particularly neutrophils, appears to be a hallmark of ST. The presence of NETs supports their pathophysiological relevance. Eosinophil recruitment suggests an allergic component to the process of ST